class_docstr: Classifier based on BERT implementation.
docstr: Add CLI args.
docstr_tok: ["Add","CLI","args","."]
code: def add_cmdline_args(
      cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
  ) -> ParlaiParser:
      """
      Add CLI args.
      """
      super().add_cmdline_args(parser, partial_opt=partial_opt)
      parser = parser.add_argument_group("BERT Classifier Arguments")
      parser.add_argument(
          "--add-cls-token",
          type="bool",
          default=True,
          help="add [CLS] token to text vec",
      )
      parser.add_argument(
          "--sep-last-utt",
          type="bool",
          default=False,
          help="separate the last utterance into a different"
          "segment with [SEP] token in between",
      )
      parser.add_argument(
          "--classifier-layers",
          nargs="+",
          type=str,
          default=None,
          help="list of classifier layers comma-separated with layer's dimension where applicable. For example: linear,64 linear,32 relu",
      )
      parser.set_defaults(dict_maxexs=0)  # skip building dictionary
      return parser
  
imports: import os
  from collections import deque
  from typing import Optional
  import torch
  from parlai.agents.bert_ranker.bert_dictionary import BertDictionaryAgent
  from parlai.agents.bert_ranker.helpers import BertWrapper, MODEL_PATH
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import History
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.utils.io import PathManager
  from parlai.utils.misc import warn_once
  from parlai.zoo.bert.build import download
label_desc: Use image node_id 3 for calling the BertClassifierAgent obj's underlying member method code with example usage: obj.add_cmdline_args(cls, parser, partial_opt) and returns: parser